Low bit-rate SNR scalable video coding based on overcomplete dictionary learning and sparse representation

2019 ◽  
Vol 31 (2) ◽  
pp. 465-489 ◽  
Author(s):  
Maziar Irannejad ◽  
Homayoun Mahdavi-Nasab
2017 ◽  
Vol 26 (6) ◽  
pp. 2972-2987 ◽  
Author(s):  
Wenrui Dai ◽  
Yangmei Shen ◽  
Hongkai Xiong ◽  
Xiaoqian Jiang ◽  
Junni Zou ◽  
...  

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Dan Grois ◽  
Ofer Hadar

This work relates to the regions-of-interest (ROI) coding that is a desirable feature in future applications based on the scalable video coding, which is an extension of the H.264/MPEG-4 AVC standard. Due to the dramatic technological progress, there is a plurality of heterogeneous devices, which can be used for viewing a variety of video content. Devices such as smartphones and tablets are mostly resource-limited devices, which make it difficult to display high-quality content. Usually, the displayed video content contains one or more ROI(s), which should be adaptively selected from the preencoded scalable video bitstream. Thus, an efficient scalable ROI video coding scheme is proposed in this work, thereby enabling the extraction of the desired regions-of-interest and the adaptive setting of the desirable ROI location, size, and resolution. In addition, an adaptive bit-rate control is provided for the region-of-interest scalable video coding. The performance of the presented techniques is demonstrated and compared with the joint scalable video model reference software (JSVM 9.19), thereby showing significant bit-rate savings as a tradeoff for the relatively low PSNR degradation.


2011 ◽  
Vol 264-265 ◽  
pp. 1631-1636
Author(s):  
Haris Al Qodri Maarif ◽  
Teddy Surya Gunawan ◽  
Afzeri

Scalable Video Coding (SVC) is the extension of the H.264/AVC standard. The SVC has good capability in video transmission systems because of its scalability which can adapt in different network conditions, especially in low bit rate transmission. The objective of this paper is to develop the low bit rate video coding for assisting the remote operation of Command and Control (CNC) milling process through virtual simulation and remote desktop interface. The algorithm for low bit rate video coding will be in the downsampling of high resolution layer before the encoding process and upsampling the high layer data in the decoder. Based on simulation results, the proposed scheme shows good performance in maintaining the video quality at 128 kbps while providing high time savings for the encoding process.


2008 ◽  
Vol 2008 ◽  
pp. 1-27 ◽  
Author(s):  
Geert Van der Auwera ◽  
Prasanth T. David ◽  
Martin Reisslein ◽  
Lina J. Karam

The recent scalable video coding (SVC) extension to the H.264/AVC video coding standard has unprecedented compression efficiency while supporting a wide range of scalability modes, including temporal, spatial, and quality (SNR) scalability, as well as combined spatiotemporal SNR scalability. The traffic characteristics, especially the bit rate variabilities, of the individual layer streams critically affect their network transport. We study the SVC traffic statistics, including the bit rate distortion and bit rate variability distortion, with long CIF resolution video sequences and compare them with the corresponding MPEG-4 Part 2 traffic statistics. We consider (i) temporal scalability with three temporal layers, (ii) spatial scalability with a QCIF base layer and a CIF enhancement layer, as well as (iii) quality scalability modes FGS and MGS. We find that the significant improvement in RD efficiency of SVC is accompanied by substantially higher traffic variabilities as compared to the equivalent MPEG-4 Part 2 streams. We find that separately analyzing the traffic of temporal-scalability only encodings gives reasonable estimates of the traffic statistics of the temporal layers embedded in combined spatiotemporal encodings and in the base layer of combined FGS-temporal encodings. Overall, we find that SVC achieves significantly higher compression ratios than MPEG-4 Part 2, but produces unprecedented levels of traffic variability, thus presenting new challenges for the network transport of scalable video.


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